The antinomies of nature and space
Bibliographic record
Abstract
Maria, Irma, Harvey, Katrina – these have become more than names. They represent several of the most recent hurricanes that have devastated communities across North America and the Caribbean. As nature–society encounters, these massive storms elevated a range of historical, sociocultural, and political economic issues to the fore – from colonialism and race, to growing patterns of inequality, government mismanagement, and the politics of knowledge related to climate change. Media coverage of these events recalls early scholarly interventions by critical disaster studies scholars that highlighted the myriad ways that ‘disasters’ are not only results of climatic or geologic forces, but are connected to historical, sociocultural, and institutional dynamics. It has become increasingly accepted that race, caste, ethnicity, income, and other patterns of inequality must be considered when evaluating the risk and outcomes of storms, earthquakes, droughts or other ‘natural’ events. Indeed, the recent aftermath of hurricanes in the Caribbean cast a spotlight on long-standing political and economic inequalities between the U.S. and its quasi-imperial territories of Puerto Rico and the Virgin Islands – whether about the pathways and futures of inequality and vulnerability on the islands, or the slow and inadequate governmental response.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".